Construction of a learning automaton for cycle detection in noisy data sequences

Abstract

This paper investigates the problem of cycle detection in periodic noisy data sequences. Our approach is based on reinforcement learning principles. A constructive approach is used to devise a variable structure learning automaton (VSLA) that becomes capable of recognizing the potential cycles of the noisy input sequence. The constructive approach allows for VSLAs to analyze sequences not requiring a priori information about their cycle and noise. Consecutive tokens of the input sequence are presented to VSLA, one at a time, where VSLA uses data's syntactic property to construct itself from a single state at the beginning to a topology that is able to recognize an unknown cycle of the given data. The main strength of this approach is applicability in many fields and high recognition rates. © Springer-Verlag Berlin Heidelberg 2005.